research
Research shows many-shot in-context learning closes gap with dedicated fine-tuning
Researchers propose Many-Shot In-Context Fine-tuning (ManyICL), a method that enables moderately-sized LLMs like Mistral 7B and Llama-3 8B to match dedicated fine-tuning performance while handling multiple downstream tasks with a single model. The approach treats in-context examples as training targets rather than prompts, significantly reducing the performance gap with task-specific models.